NDLI: Automated detection of Southeast Asian Ovalocytosis (SAO) obtained from thin blood smear microphotographs

Content Provider	IEEE Xplore Digital Library
Author	Eunike Sawitning Ayu Setyono Nugroho, Anto Satriyo Ughi, Fuad Gunawan, Made Pragesjvara, Vitria Rozi, Ismail Ekoprayitno Puji Budi Setia Asih
Copyright Year	2015
Description	Author affiliation: Department of Biomedical Engineering, Faculty of Life Sciences and Technology, Swiss German University, EduTown BSD City, Tangerang, Indonesia, 15339 (Eunike Sawitning Ayu Setyono; Ughi, Fuad) \|\| Eijkman Institute for Molecular Biology, Jalan Diponegoro no. 69, Jakarta, Indonesia, 10430 (Rozi, Ismail Ekoprayitno; Puji Budi Setia Asih) \|\| Center for Information & Communication Technology, Agency for the Assessment & Application of Technology/Teknologi 3 Bld., 3F, PUSPIPTEK Serpong, Tangerang Selatan, Indonesia, 15314 (Nugroho, Anto Satriyo; Gunawan, Made; Pragesjvara, Vitria)
Abstract	Southeast Asian Ovalocytosis (SAO) is an erythrocyte disorder, which is characterized by oval-shaped cells with one or two transverse ridges or a longitudinal slit on blood smears. To check the SAO using thin blood smear requires expert manpower and is time consuming. This research was aimed to automatically detect the SAO from thin blood smear images. Digital images were acquired using a digital camera connected to a light microscope. Images underwent gray-scale conversion to decrease representation of images. Otsu?s Method was implemented to separate blood components and the background. All clumps present in the image were then processed to be separated using recursive bottleneck detection algorithm. The major and minor axes of each extracted erythrocyte were then obtained for the calculation of ratio. The ratio determined whether the erythrocyte is normal or oval, and a count was kept between them. The percentage of oval erythrocyte over total erythrocyte was used as the determination of SAO positive or negative. The major and minor axes of the erythrocytes were successfully detected with error rate lower than 1%. Thin blood smear sample, prepared and provided by Eijkman Institute for Molecular Biology Indonesia, were used to build and test of the proposed algorithm.
Starting Page	70
Ending Page	74
File Size	352427
Page Count	5
File Format	PDF
e-ISBN	9781467378000
DOI	10.1109/ICICI-BME.2015.7401337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-02
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clump splitting Southeast Asian Ovalocytosis SAO Microscopy Image processing Feature extraction Classification algorithms Detection algorithms Blood Thin blood smear Diseases Biomedical engineering
Content Type	Text
Resource Type	Article

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